http://localhost:8080/xmlui/handle/123456789/1259 2017-18 Research paper published Tue, 23 Jun 2026 06:28:16 GMT 2026-06-23T06:28:16Z http://localhost:8080/xmlui/handle/123456789/1263 Title: Optimizing Predictive Modelling of Customer Behaviour Using Simulated Annealing Authors: Gangurde, Roshan Abstract: Customer behaviour modelling is an important data mining approach for making operational and strategic decisions. Market Basket Analysis (MBA) is used to determine products that customers purchase together. Knowing the products that a customer will shop as a group is very helpful to a retailer. The business can use this information in predicting sales at the right time, at the right place, and for the right customer. Moreover, Company Marketers can use the basket analysis results to determine new products to serve their existing loyal customers. In the present paper, Genetic Algorithm is used to determine the population of solutions, which consumes more time to produce a solution. In this paper, it is proposed to use the Extended HCleaner Algorithm to remove noisy data from the datasets. The pre-processed dataset is then submitted to an ANN model. From the ANN model, weights of the products are determined by an association. The products which get maximum weights are sent to Apriori algorithm, which calculates the optimal combination of the products. The solution of Apriori algorithm is sent to next level of the predictive model. It contains two stages: (1) Similarity calculation through cosine similaritytechnique and (2) Simulated annealing. The cosine similarity is used to obtain the association between products which is further sent to simulated annealing algorithm to find the single solution through association rules. Simulated annealing is used to minimize the response time. It is more effective than the existing system. Tue, 01 May 2018 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/1263 2018-05-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/1262 Title: Optimizing Predictive Modelling of Customer Behaviour Using Simulated Annealing Authors: Gangurde, Roshan Abstract: Customer behaviour modelling is an important data mining approach for making operational and strategic decisions. Market Basket Analysis (MBA) is used to determine products that customers purchase together. Knowing the products that a customer will shop as a group is very helpful to a retailer. The business can use this information in predicting sales at the right time, at the right place, and for the right customer. Moreover, Company Marketers can use the basket analysis results to determine new products to serve their existing loyal customers. In the present paper, Genetic Algorithm is used to determine the population of solutions, which consumes more time to produce a solution. In this paper, it is proposed to use the Extended HCleaner Algorithm to remove noisy data from the datasets. The pre-processed dataset is then submitted to an ANN model. From the ANN model, weights of the products are determined by an association. The products which get maximum weights are sent to Apriori algorithm, which calculates the optimal combination of the products. The solution of Apriori algorithm is sent to next level of the predictive model. It contains two stages: (1) Similarity calculation through cosine similarity technique and (2) Simulated annealing. The cosine similarity is used to obtain the association between products which is further sent to simulated annealing algorithm to find the single solution through association rules. Simulated annealing is used to minimize the response time. It is more effective than the existing system. Mon, 01 Jan 2018 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/1262 2018-01-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/1261 Title: Optimized Predictive Model Using Artificial Neural Network for Market Basket Analysis Authors: Gangurde, Roashan Abstract: Market Basket Analysis (MBA) is a modeling technique in view of the theory that in the event that you purchase a specific items, you are progressively likely to purchase another items. The changing requests of the consumer with estimation of seasons are the main task against the market basket analysis. MBA is nothing but predictive model which is used to predict the buyer’s behaviour with goal of finding the relationship among various products from their market basket. The optimization in finding of such relationships can help the retailers and merchants to design a sales strategy by considering the items frequently purchased together by customers. Regardless of benefits of using MBA, there are some major research challenges associated with the MBA designing in previous methods. As there is significant growth of online shopping portals and product purchase now days, the current predictive models are ineffective and inefficient over large sales datasets. In this paper, we are attempting to design optimized predictive model to overcome the current research problems. We proposed novel predictive model for MBA by using data cleaning and neural network approach. Our designed data cleaning method helps to improve the quality of input dataset and hence MBA results by removing the all types of errors from it. Secondly unsupervised machine learning based MBA model based on artificial neural network designed. The existing Apriori algorithm is modified by using neural network method in order to optimize the prediction results. To the best of our knowledge, this is the first attempt in MBA. The practical results showing that proposed predictive model for MBA outperforming the previous method Fri, 01 Sep 2017 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/1261 2017-09-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/1260 Title: Building Prediction Model using Market Basket Analysis Authors: Gangurde, Roshan Abstract: In the recent years, analyzing shopping baskets turned out to be very appealing to retailers. Sophisticated technology made it possible for them to collect information of their customers and what they purchase. The introduction of electronic point-of-sale expanded the utilization and application of transactional data in Market Basket Analysis (MBA). In retail business, analyzing such information is exceedingly valuable for understanding purchasing behavior. Mining purchasing patterns allows retailers to adjust promotions, store settings and serve consumers better. Predictive analysis is an advanced branch of data engineering which generally predicts some occurrence or probability based on data. Predictive analytics uses data-mining techniques in order to make predictions about future events, and make recommendations based on these predictions. The process involves an analysis of historic data and based on that analysis to predict the future occurrences or events. A model can be created to predict using Predictive Analytics modelling techniques. The form of these predictive models varies depending on the data they are using. Predictive Analytics is composed of various statistical & analytical techniques used to develop models that will predict future occurrence, events or probabilities. Predictive analytics is able to not only deal with continuous changes, but discontinuous changes as well. Classification, prediction, and to some extent, affinity analysis constitute the analytical methods employed in predictive analytics. Wed, 01 Feb 2017 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/1260 2017-02-01T00:00:00Z